A curated reading list for those starting to learn about Large Language Models (LLMs), covering foundational concepts, practical applications, and future trends, updated for 2026.
This article explores the field of mechanistic interpretability, aiming to understand how large language models (LLMs) work internally by reverse-engineering their computations. It discusses techniques for identifying and analyzing the functions of individual neurons and circuits within these models, offering insights into their decision-making processes.
Think of Continuous AI as background agents that operate in your repository for tasks that require reasoning.
>Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.”
> “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.”
>“Flag performance regressions in critical paths.”
>“Detect semantic regressions in user flows.”
>Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.”
> “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.”
>“Flag performance regressions in critical paths.”
>“Detect semantic regressions in user flows.”
This guide explains how to use tool calling with local LLMs, including examples with mathematical, story, Python code, and terminal functions, using llama.cpp, llama-server, and OpenAI endpoints.
Vercel has released Skills.sh, an open-source tool designed to provide AI agents with a standardized way to execute reusable actions, or skills, through the command line. The project introduces what Vercel describes as an open agent skills ecosystem, where developers can define, share, and run discrete operations that agents can invoke as part of their workflows.
Agent Trace is an open specification for tracking AI-generated code, providing a vendor-neutral format for recording AI contributions alongside human authorship in version-controlled codebases.
Qwen3-Coder-Next is an 80B MoE model with 256K context designed for fast, agentic coding and local use. It offers performance comparable to models with 10-20x more active parameters and excels in long-horizon reasoning, complex tool use, and recovery from execution failures.
The article details the release of Qwen3-Coder-Next, a new 80-billion-parameter open-source large language model (LLM) from Alibaba’s Qwen team. This model is designed for coding tasks and utilizes an ultra-sparse Mixture-of-Experts (MoE) architecture, activating only 3 billion parameters at a time for increased efficiency. It boasts a massive 262,144 token context window and innovative techniques like Gated DeltaNet and Best-Fit Packing to overcome traditional LLM limitations. Qwen3-Coder-Next was trained using an "agentic training" pipeline, learning from real-world coding scenarios and feedback. It supports 370 programming languages and demonstrates competitive performance against leading models like OpenAI’s Codex and Anthropic’s Claude, while also exhibiting strong security features. The release is positioned as a significant advancement in open-weight AI and a challenge to proprietary coding models.
This article details seven advanced feature engineering techniques using LLM embeddings to improve machine learning model performance. It covers techniques like dimensionality reduction, semantic similarity, clustering, and more.
The article explores how to leverage LLM embeddings for advanced feature engineering in machine learning, going beyond simple similarity searches. It details seven techniques:
1. **Embedding Arithmetic:** Performing mathematical operations (addition, subtraction) on embeddings to represent concepts like "positive sentiment - negative sentiment = overall sentiment".
2. **Embedding Clustering:** Using clustering algorithms (like k-means) on embeddings to create categorical features representing groups of similar text.
3. **Embedding Dimensionality Reduction:** Reducing the dimensionality of embeddings using techniques like PCA or UMAP to create more compact features while preserving important information.
4. **Embedding as Input to Tree-Based Models:** Directly using embedding vectors as features in tree-based models like Random Forests or Gradient Boosting. The article highlights the importance of careful handling of high-dimensional data.
5. **Embedding-Weighted Averaging:** Calculating weighted averages of embeddings based on relevance scores (e.g., TF-IDF) to create a single, representative embedding for a document.
6. **Embedding Difference:** Calculating the difference between embeddings to capture changes or relationships between texts (e.g., before/after edits, question/answer pairs).
7. **Embedding Concatenation:** Combining multiple embeddings (e.g., title and body of a document) to create a richer feature representation.
The article explores how to leverage LLM embeddings for advanced feature engineering in machine learning, going beyond simple similarity searches. It details seven techniques:
1. **Embedding Arithmetic:** Performing mathematical operations (addition, subtraction) on embeddings to represent concepts like "positive sentiment - negative sentiment = overall sentiment".
2. **Embedding Clustering:** Using clustering algorithms (like k-means) on embeddings to create categorical features representing groups of similar text.
3. **Embedding Dimensionality Reduction:** Reducing the dimensionality of embeddings using techniques like PCA or UMAP to create more compact features while preserving important information.
4. **Embedding as Input to Tree-Based Models:** Directly using embedding vectors as features in tree-based models like Random Forests or Gradient Boosting. The article highlights the importance of careful handling of high-dimensional data.
5. **Embedding-Weighted Averaging:** Calculating weighted averages of embeddings based on relevance scores (e.g., TF-IDF) to create a single, representative embedding for a document.
6. **Embedding Difference:** Calculating the difference between embeddings to capture changes or relationships between texts (e.g., before/after edits, question/answer pairs).
7. **Embedding Concatenation:** Combining multiple embeddings (e.g., title and body of a document) to create a richer feature representation.
This post discusses the limitations of using cosine similarity for compatibility matching, specifically in the context of a dating app. The author found that high cosine similarity scores didn't always translate to actual compatibility due to the inability of embeddings to capture dealbreaker preferences. They improved results by incorporating structured features and hard filters.
